Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks

Autonomous robots need to interact with unknown, unstructured and changing environments, constantly facing novel challenges. Therefore, continuous online adaptation for lifelong-learning and the need of sample-efficient mechanisms to adapt to changes in the environment, the constraints, the tasks, or the robot itself are crucial. In this work, we propose a novel framework for probabilistic online motion planning with online adaptation based on a bio-inspired stochastic recurrent neural network. By using learning signals which mimic the intrinsic motivation signalcognitive dissonance in addition with a mental replay strategy to intensify experiences, the stochastic recurrent network can learn from few physical interactions and adapts to novel environments in seconds. We evaluate our online planning and adaptation framework on an anthropomorphic KUKA LWR arm. The rapid online adaptation is shown by learning unknown workspace constraints sample-efficiently from few physical interactions while following given way points.Comment: accepted in Neural Network

Developing a targeted English-language curriculum and materials for Latino caregivers of infants with special needs as part of a NICU pre-discharge education program

poster abstractAbstract: Latino infants with special healthcare needs are at high risk of mortality and have difficulty obtaining specialty care. Poor English-language skills of the caregivers add an additional layer of vulnerability. Existing health-related English-language programs address adult, but not pediatric health concerns. A clear need exists for short-term health-related English-language education programs to develop survival communication skills in low-literacy Limited English Proficiency (LEP) caregivers. To fill this need for intervention, the International Center for Intercultural Communication (ICIC) at IUPUI collaborated with Family Voices Indiana, a family advocacy group, and created a grant-funded series of classes to be taught in a one-on-one setting at Riley Hospital for Latino LEP parents of Neonatal Intensive Care Unit (NICU) babies. The goal of the study is to provide these parents with the English language competency to take an active part in the medical decision making and care of their children. An additional goal of the program is to improve families' ability to enroll in local English as Second Language programs in the community. The findings will be discussed in three parts: We will first feature the needs-analysis period followed by the development of a curriculum, instructional materials, and pre-post intervention assessments based on the identified needs. Second, we will feature the actual intervention and will involve cases from working with low-literacy/low-proficiency caregivers. Third, we will discuss the post-intervention stage and feature data analysis with the purpose of assessing the viability of the curriculum and materials that would lead to revisions. The project is designed to ensure eventual adaptability of the curriculum for ESL caregivers of various language backgrounds, stronger language or literacy skills, a variety of healthcare contexts, and the larger pediatric population

4D Cardiac Volume Reconstruction from Free-Breathing 2D Real-Time Image Acquisitions using Iterative Motion Correction

For diagnosis, treatment and study of various cardiac diseases directly affecting the functionality and morphology of the heart, physicians rely more and more on MR imaging techniques. MRI has good tissue contrast and can achieve high spatial and temporal resolutions. However it requires a relatively long time to obtain enough data to reconstruct useful images. Additionally, when imaging the heart, the occurring motions - breathing and heart beat - have to be taken into account. While the cardiac motion still has to be correctly seen to asses functionality, the respiratory motion has to be removed to avoid serious motion artefacts. We present initial results for a reconstruction pipeline that takes multiple stacks of 2D slices, calculates the occurring deformations for both cardiac and respiratory motions and reconstructs a coherent 4D volume of the beating heart. The 2D slices are acquired during free-breathing over the whole respiratory cycle, using a fast real-time technique. For motion estimation two different transformation models were used. A cyclic 4D B-spline free-form deformation model for the cardiac motion and a 1D B-spline affine model for the respiratory motion. Both transformations and the common reference frame needed for the registration are optimized in an interleaved, iterative scheme

Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRI

In this work, we tackle the problem of Semi-Supervised Anomaly Segmentation (SAS) in Magnetic Resonance Images (MRI) of the brain, which is the task of automatically identifying pathologies in brain images. Our work challenges the effectiveness of current Machine Learning (ML) approaches in this application domain by showing that thresholding Fluid-attenuated inversion recovery (FLAIR) MR scans provides better anomaly segmentation maps than several different ML-based anomaly detection models. Specifically, our method achieves better Dice similarity coefficients and Precision-Recall curves than the competitors on various popular evaluation data sets for the segmentation of tumors and multiple sclerosis lesions.Comment: 10 pages, 4 figures, accepted to the MICCAI 2021 BrainLes Worksho

A Review of Causality for Learning Algorithms in Medical Image Analysis

Medical image analysis is a vibrant research area that offers doctors and medical practitioners invaluable insight and the ability to accurately diagnose and monitor disease. Machine learning provides an additional boost for this area. However, machine learning for medical image analysis is particularly vulnerable to natural biases like domain shifts that affect algorithmic performance and robustness. In this paper we analyze machine learning for medical image analysis within the framework of Technology Readiness Levels and review how causal analysis methods can fill a gap when creating robust and adaptable medical image analysis algorithms. We review methods using causality in medical imaging AI/ML and find that causal analysis has the potential to mitigate critical problems for clinical translation but that uptake and clinical downstream research has been limited so far.Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://www.melba-journal.org/papers/2022:028.html". ; Paper ID: 2022:02